Fix dashes, quotes, spacing, etc.

diff --git a/hpcc.tex b/hpcc.tex
index 5226bc31f87800932802e445ad855a19367657fd..497ed68987dbf2a5f0b0d7a9bf78f6988095a3f4 100644 (file)
--- a/hpcc.tex
+++ b/hpcc.tex
@@ -105,7 +105,7 @@ problems raised by  researchers on various scientific disciplines but also by in
 increasing complexity of these requested  applications combined with a continuous increase of their sizes lead to  write
 distributed and parallel algorithms requiring significant hardware  resources (grid computing, clusters, broadband
 network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient
 increasing complexity of these requested  applications combined with a continuous increase of their sizes lead to  write
 distributed and parallel algorithms requiring significant hardware  resources (grid computing, clusters, broadband
 network, etc.) but also a non-negligible CPU execution time. We consider in this paper a class of highly efficient

-parallel algorithms called \texttt{numerical iterative algorithms} executed in a distributed environment. As their name
+parallel algorithms called \emph{numerical iterative algorithms} executed in a distributed environment. As their name

 suggests, these algorithm solves a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 demonstrate the convergence of these algorithms \cite{BT89,Bahi07}. 
 suggests, these algorithm solves a given problem by successive iterations ($X_{n +1} = f(X_{n})$) from an initial value
 $X_{0}$ to find an approximate value $X^*$ of the solution with a very low residual error. Several well-known methods
 demonstrate the convergence of these algorithms \cite{BT89,Bahi07}. 


@@ -115,7 +115,7 @@ be solved in parallel on multiple processing units. The latter will communicate
 iteration starts and until the approximate solution is reached. These parallel  computations can be performed either in
 \emph{synchronous} mode where a new iteration begin only when all nodes communications are completed,
 either \emph{asynchronous} mode where processors can continue independently without or few synchronization points. For
 iteration starts and until the approximate solution is reached. These parallel  computations can be performed either in
 \emph{synchronous} mode where a new iteration begin only when all nodes communications are completed,
 either \emph{asynchronous} mode where processors can continue independently without or few synchronization points. For

-instance in the \textit{Asynchronous Iterations - Asynchronous   Communications (AIAC)} model \cite{bcvc06:ij}, local
+instance in the \textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model \cite{bcvc06:ij}, local

 computations do not need to wait for required data. Processors can then perform their iterations with the data present
 at that time. Even if the number of iterations required before the convergence is generally greater than for the
 synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to
 computations do not need to wait for required data. Processors can then perform their iterations with the data present
 at that time. Even if the number of iterations required before the convergence is generally greater than for the
 synchronous case, AIAC algorithms can significantly reduce overall execution times by suppressing idle times due to


@@ -160,15 +160,15 @@ carried out will be presented before some concluding remarks and future works.
 
 As exposed in the introduction, parallel iterative methods are now widely used in many scientific domains. They can be
 classified in three main classes depending on how iterations and communications are managed (for more details readers
 
 As exposed in the introduction, parallel iterative methods are now widely used in many scientific domains. They can be
 classified in three main classes depending on how iterations and communications are managed (for more details readers

-can refer to \cite{bcvc06:ij}). In the \textit{Synchronous Iterations - Synchronous Communications (SISC)} model data
+can refer to \cite{bcvc06:ij}). In the \textit{Synchronous Iterations~-- Synchronous Communications (SISC)} model data

 are exchanged at the end of each iteration. All the processors must begin the same iteration at the same time and
 are exchanged at the end of each iteration. All the processors must begin the same iteration at the same time and

-important idle times on processors are generated. The \textit{Synchronous Iterations - Asynchronous Communications
+important idle times on processors are generated. The \textit{Synchronous Iterations~-- Asynchronous Communications

 (SIAC)} model can be compared to the previous one except that data required on another processor are sent asynchronously
 i.e.  without stopping current computations. This technique allows to partially overlap communications by computations
 but unfortunately, the overlapping is only partial and important idle times remain.  It is clear that, in a grid
 computing context, where the number of computational nodes is large, heterogeneous and widely distributed, the idle
 times generated by synchronizations are very penalizing. One way to overcome this problem is to use the
 (SIAC)} model can be compared to the previous one except that data required on another processor are sent asynchronously
 i.e.  without stopping current computations. This technique allows to partially overlap communications by computations
 but unfortunately, the overlapping is only partial and important idle times remain.  It is clear that, in a grid
 computing context, where the number of computational nodes is large, heterogeneous and widely distributed, the idle
 times generated by synchronizations are very penalizing. One way to overcome this problem is to use the

-\textit{Asynchronous Iterations - Asynchronous   Communications (AIAC)} model. Here, local computations do not need to
+\textit{Asynchronous Iterations~-- Asynchronous Communications (AIAC)} model. Here, local computations do not need to

 wait for required data. Processors can then perform their iterations with the data present at that time. Figure
 \ref{fig:aiac} illustrates this model where the gray blocks represent the computation phases, the white spaces the idle
 times and the arrows the communications. With this algorithmic model, the number of iterations required before the
 wait for required data. Processors can then perform their iterations with the data present at that time. Figure
 \ref{fig:aiac} illustrates this model where the gray blocks represent the computation phases, the white spaces the idle
 times and the arrows the communications. With this algorithmic model, the number of iterations required before the


@@ -179,7 +179,7 @@ in a grid computing context.
 \begin{figure}[!t]
   \centering
     \includegraphics[width=8cm]{AIAC.pdf}
 \begin{figure}[!t]
   \centering
     \includegraphics[width=8cm]{AIAC.pdf}

-  \caption{The Asynchronous Iterations - Asynchronous Communications model } 
+  \caption{The Asynchronous Iterations~-- Asynchronous Communications model}

   \label{fig:aiac}
 \end{figure}
 
   \label{fig:aiac}
 \end{figure}
 


@@ -328,7 +328,7 @@ and with the addition of the primitive MPI\_Test was needed to avoid a memory fa
 functions optimizer for memory footprint and CPU usage is not recommended knowing that one wants to get real results by simulation.
 As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, all declared 
 global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of 
 functions optimizer for memory footprint and CPU usage is not recommended knowing that one wants to get real results by simulation.
 As mentioned, upon this adaptation, the algorithm is executed as in the real life in the simulated environment after the following minor changes. First, all declared 
 global variables have been moved to local variables for each subroutine. In fact, global variables generate side effects arising from the concurrent access of 

-shared memory used by threads simulating each computing units in the SimGrid architecture. Second, the alignment of certain types of variables such as "long int" had 
+shared memory used by threads simulating each computing units in the SimGrid architecture. Second, the alignment of certain types of variables such as ``long int'' had

 also to be reviewed. Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
 In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real 
 environment. We have tested in synchronous mode with a simulated platform starting from a modest 2 or 3 clusters grid to a larger configuration like simulating 
 also to be reviewed. Finally, some compilation errors on MPI\_Waitall and MPI\_Finalize primitives have been fixed with the latest version of SimGrid.
 In total, the initial MPI program running on the simulation environment SMPI gave after a very simple adaptation the same results as those obtained in a real 
 environment. We have tested in synchronous mode with a simulated platform starting from a modest 2 or 3 clusters grid to a larger configuration like simulating 


@@ -540,7 +540,7 @@ matrix size of $62$ elements, equality between the performance of the two modes
 (synchronous and asynchronous) is achieved with an inter cluster of
 \np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency by
 \np[\%]{78} with a matrix size of $100$ points, it was necessary to degrade the
 (synchronous and asynchronous) is achieved with an inter cluster of
 \np[Mbit/s]{10} and a latency of \np[ms]{E-1}. To challenge an efficiency by
 \np[\%]{78} with a matrix size of $100$ points, it was necessary to degrade the

-inter cluster network bandwidth from 5 to 2 Mbit/s.
+inter cluster network bandwidth from 5 to \np[Mbit/s]{2}.

 
 A last attempt was made for a configuration of three clusters but more powerful
 with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was
 
 A last attempt was made for a configuration of three clusters but more powerful
 with 200 nodes in total. The convergence with a speedup of \np[\%]{90} was